Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
Springer International Publishing (Verlag)
978-3-030-32247-2 (ISBN)
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019.
The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections:
Part I: optical imaging; endoscopy; microscopy.
Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression.
Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging.
Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis.
Part V: computer assisted interventions; MIC meets CAI.
Part VI: computed tomography; X-ray imaging.
Neuroimage Reconstruction and Synthesis.- Isotropic MRI Super-Resolution Reconstruction with Multi-Scale Gradient Field Prior.- A Two-Stage Multi-Loss Super-Resolution Network For Arterial Spin Labeling Magnetic Resonance Imaging.- Model Learning: Primal Dual Networks for Fast MR imaging.- Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging.- Joint Reconstruction of PET + Parallel-MRI in a Bayesian Coupled-Dictionary MRF Framework.- Deep Learning Based Framework for Direct Reconstruction of PET Images.- Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction.- Reconstruction of Isotropic High-Resolution MR Image from Multiple Anisotropic Scans using Sparse Fidelity Loss and Adversarial Regularization.- Single Image Based Reconstruction of High Field-like MR Images.- Deep Neural Network for QSM Background Field Removal.- RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting.- RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting.- GANReDL: Medical Image enhancement using a generative adversarial network with real-order derivative induced loss functions.- Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks.- Semi-Supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control.- Disease-Image Specific Generative Adversarial Network for Brain Disease Diagnosis with Incomplete Multi-Modal Neuroimages.- Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map.- CoCa-GAN: Common-feature-learning-based Context-aware Generative Adversarial Network for Glioma Grading.- Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression.- Neuroimage Segmentation.- Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation.- 3D DilatedMulti-Fiber Network for Real-time Brain Tumor Segmentation in MRI.- Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants.- VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation.- Weakly Supervised Brain Lesion Segmentation via Attentional Representation Learning.- Scalable Neural Architecture Search for 3D Medical Image Segmentation.- Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images.- High Resolution Medical Image Segmentation using Data-swapping Method.- X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies.- Multi-View Semi-supervised 3D Whole Brain Segmentation with a Self-Ensemble Network.- CLCI-Net: Cross-Level Fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke.- Brain Segmentation from k-space with End-to-end Recurrent Attention Network.- Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images.- CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion.- A Joint 3D+2D Fully Convolutional Framework for Subcortical Segmentation.- U-ReSNet: Ultimate coupling of Registration and Segmentation with deep Nets.- Generative adversarial network for segmentation of motion affected neonatal brain MRI.- Interactive deep editing framework for medical image segmentation.- Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices.- Improving Multi-Atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation.- Unsupervised deep learning for Bayesian brain MRI segmentation.- Online atlasing using an iterative centroid.- ARS-Net: Adaptively Rectified Supervision Network for Automated 3D Ultrasound Image Segmentation.- Complete Fetal Head Compounding from Multi-View 3D Ultrasound.- SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization fo
| Erscheinungsdatum | 20.10.2019 |
|---|---|
| Reihe/Serie | Image Processing, Computer Vision, Pattern Recognition, and Graphics | Lecture Notes in Computer Science |
| Zusatzinfo | XXXVIII, 888 p. 359 illus., 314 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 1395 g |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Technik | |
| Schlagworte | Applications • Artificial Intelligence • Computed tomography • Computer Aided Diagnosis • computer assisted interventions • Computer Science • conference proceedings • Image Processing • image reconstruction • Image Segmentation • Imaging Systems • Informatics • Learning Algorithms • machine learning • Medical Images • Neural networks • neuroimage reconstruction • neuroimage segmentation • Optical imaging • Research • segmentation methods • Support Vector Machines • SVM • x-ray imaging |
| ISBN-10 | 3-030-32247-5 / 3030322475 |
| ISBN-13 | 978-3-030-32247-2 / 9783030322472 |
| Zustand | Neuware |
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